{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Image features exercise\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "We have seen that we can achieve reasonable performance on an image classification task by training a linear classifier on the pixels of the input image. In this exercise we will show that we can improve our classification performance by training linear classifiers not on raw pixels but on features that are computed from the raw pixels.\n",
    "\n",
    "All of your work for this exercise will be done in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading extenrnal modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Load data\n",
    "Similar to previous exercises, we will load CIFAR-10 data from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "from cs231n.features import color_histogram_hsv, hog_feature\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "    # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "    try:\n",
    "       del X_train, y_train\n",
    "       del X_test, y_test\n",
    "       print('Clear previously loaded data.')\n",
    "    except:\n",
    "       pass\n",
    "\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "    \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "    \n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "## Extract Features\n",
    "For each image we will compute a Histogram of Oriented\n",
    "Gradients (HOG) as well as a color histogram using the hue channel in HSV\n",
    "color space. We form our final feature vector for each image by concatenating\n",
    "the HOG and color histogram feature vectors.\n",
    "\n",
    "Roughly speaking, HOG should capture the texture of the image while ignoring\n",
    "color information, and the color histogram represents the color of the input\n",
    "image while ignoring texture. As a result, we expect that using both together\n",
    "ought to work better than using either alone. Verifying this assumption would\n",
    "be a good thing to try for your own interest.\n",
    "\n",
    "The `hog_feature` and `color_histogram_hsv` functions both operate on a single\n",
    "image and return a feature vector for that image. The extract_features\n",
    "function takes a set of images and a list of feature functions and evaluates\n",
    "each feature function on each image, storing the results in a matrix where\n",
    "each column is the concatenation of all feature vectors for a single image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true,
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done extracting features for 1000 / 49000 images\n",
      "Done extracting features for 2000 / 49000 images\n",
      "Done extracting features for 3000 / 49000 images\n",
      "Done extracting features for 4000 / 49000 images\n",
      "Done extracting features for 5000 / 49000 images\n",
      "Done extracting features for 6000 / 49000 images\n",
      "Done extracting features for 7000 / 49000 images\n",
      "Done extracting features for 8000 / 49000 images\n",
      "Done extracting features for 9000 / 49000 images\n",
      "Done extracting features for 10000 / 49000 images\n",
      "Done extracting features for 11000 / 49000 images\n",
      "Done extracting features for 12000 / 49000 images\n",
      "Done extracting features for 13000 / 49000 images\n",
      "Done extracting features for 14000 / 49000 images\n",
      "Done extracting features for 15000 / 49000 images\n",
      "Done extracting features for 16000 / 49000 images\n",
      "Done extracting features for 17000 / 49000 images\n",
      "Done extracting features for 18000 / 49000 images\n",
      "Done extracting features for 19000 / 49000 images\n",
      "Done extracting features for 20000 / 49000 images\n",
      "Done extracting features for 21000 / 49000 images\n",
      "Done extracting features for 22000 / 49000 images\n",
      "Done extracting features for 23000 / 49000 images\n",
      "Done extracting features for 24000 / 49000 images\n",
      "Done extracting features for 25000 / 49000 images\n",
      "Done extracting features for 26000 / 49000 images\n",
      "Done extracting features for 27000 / 49000 images\n",
      "Done extracting features for 28000 / 49000 images\n",
      "Done extracting features for 29000 / 49000 images\n",
      "Done extracting features for 30000 / 49000 images\n",
      "Done extracting features for 31000 / 49000 images\n",
      "Done extracting features for 32000 / 49000 images\n",
      "Done extracting features for 33000 / 49000 images\n",
      "Done extracting features for 34000 / 49000 images\n",
      "Done extracting features for 35000 / 49000 images\n",
      "Done extracting features for 36000 / 49000 images\n",
      "Done extracting features for 37000 / 49000 images\n",
      "Done extracting features for 38000 / 49000 images\n",
      "Done extracting features for 39000 / 49000 images\n",
      "Done extracting features for 40000 / 49000 images\n",
      "Done extracting features for 41000 / 49000 images\n",
      "Done extracting features for 42000 / 49000 images\n",
      "Done extracting features for 43000 / 49000 images\n",
      "Done extracting features for 44000 / 49000 images\n",
      "Done extracting features for 45000 / 49000 images\n",
      "Done extracting features for 46000 / 49000 images\n",
      "Done extracting features for 47000 / 49000 images\n",
      "Done extracting features for 48000 / 49000 images\n",
      "Done extracting features for 49000 / 49000 images\n"
     ]
    }
   ],
   "source": [
    "from cs231n.features import *\n",
    "\n",
    "num_color_bins = 10 # Number of bins in the color histogram\n",
    "feature_fns = [hog_feature, lambda img: color_histogram_hsv(img, nbin=num_color_bins)]\n",
    "X_train_feats = extract_features(X_train, feature_fns, verbose=True)\n",
    "X_val_feats = extract_features(X_val, feature_fns)\n",
    "X_test_feats = extract_features(X_test, feature_fns)\n",
    "\n",
    "# Preprocessing: Subtract the mean feature\n",
    "mean_feat = np.mean(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats -= mean_feat\n",
    "X_val_feats -= mean_feat\n",
    "X_test_feats -= mean_feat\n",
    "\n",
    "# Preprocessing: Divide by standard deviation. This ensures that each feature\n",
    "# has roughly the same scale.\n",
    "std_feat = np.std(X_train_feats, axis=0, keepdims=True)\n",
    "X_train_feats /= std_feat\n",
    "X_val_feats /= std_feat\n",
    "X_test_feats /= std_feat\n",
    "\n",
    "# Preprocessing: Add a bias dimension\n",
    "X_train_feats = np.hstack([X_train_feats, np.ones((X_train_feats.shape[0], 1))])\n",
    "X_val_feats = np.hstack([X_val_feats, np.ones((X_val_feats.shape[0], 1))])\n",
    "X_test_feats = np.hstack([X_test_feats, np.ones((X_test_feats.shape[0], 1))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train SVM on features\n",
    "Using the multiclass SVM code developed earlier in the assignment, train SVMs on top of the features extracted above; this should achieve better results than training SVMs directly on top of raw pixels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-09 reg 5.000000e+04 train accuracy: 0.093020 val accuracy: 0.083000\n",
      "lr 1.000000e-09 reg 5.000000e+05 train accuracy: 0.093245 val accuracy: 0.083000\n",
      "lr 1.000000e-09 reg 5.000000e+06 train accuracy: 0.183612 val accuracy: 0.186000\n",
      "lr 1.000000e-08 reg 5.000000e+04 train accuracy: 0.415224 val accuracy: 0.418000\n",
      "lr 1.000000e-08 reg 5.000000e+05 train accuracy: 0.414612 val accuracy: 0.418000\n",
      "lr 1.000000e-08 reg 5.000000e+06 train accuracy: 0.418020 val accuracy: 0.423000\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.410878 val accuracy: 0.416000\n",
      "lr 1.000000e-07 reg 5.000000e+05 train accuracy: 0.411143 val accuracy: 0.417000\n",
      "lr 1.000000e-07 reg 5.000000e+06 train accuracy: 0.380122 val accuracy: 0.379000\n",
      "best validation accuracy achieved during cross-validation: 0.423000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune the learning rate and regularization strength\n",
    "\n",
    "from cs231n.classifiers.linear_classifier import LinearSVM\n",
    "\n",
    "learning_rates = [1e-9, 1e-8, 1e-7]\n",
    "regularization_strengths = [5e4, 5e5, 5e6]\n",
    "\n",
    "results = {}\n",
    "best_val = -1\n",
    "best_svm = None\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Use the validation set to set the learning rate and regularization strength. #\n",
    "# This should be identical to the validation that you did for the SVM; save    #\n",
    "# the best trained classifer in best_svm. You might also want to play          #\n",
    "# with different numbers of bins in the color histogram. If you are careful    #\n",
    "# you should be able to get accuracy of near 0.44 on the validation set.       #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "svm = LinearSVM()\n",
    "for lr in learning_rates:\n",
    "    for rs in regularization_strengths:\n",
    "        loss_hist = svm.train(X_train_feats, y_train, learning_rate=lr, reg=rs, num_iters=1500)\n",
    "        \n",
    "        y_train_pred = svm.predict(X_train_feats)\n",
    "        train_acc = np.mean(y_train == y_train_pred)\n",
    "        \n",
    "        y_val_pred = svm.predict(X_val_feats)\n",
    "        val_acc = np.mean(y_val == y_val_pred)\n",
    "        \n",
    "        results[(lr, rs)] = (train_acc, val_acc)\n",
    "        if (val_acc > best_val):\n",
    "            best_val = val_acc\n",
    "            best_svm = svm\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.392\n"
     ]
    }
   ],
   "source": [
    "# Evaluate your trained SVM on the test set\n",
    "y_test_pred = best_svm.predict(X_test_feats)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print(test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 80 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# An important way to gain intuition about how an algorithm works is to\n",
    "# visualize the mistakes that it makes. In this visualization, we show examples\n",
    "# of images that are misclassified by our current system. The first column\n",
    "# shows images that our system labeled as \"plane\" but whose true label is\n",
    "# something other than \"plane\".\n",
    "\n",
    "examples_per_class = 8\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for cls, cls_name in enumerate(classes):\n",
    "    idxs = np.where((y_test != cls) & (y_test_pred == cls))[0]\n",
    "    idxs = np.random.choice(idxs, examples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)\n",
    "        plt.imshow(X_test[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls_name)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "### Inline question 1:\n",
    "Describe the misclassification results that you see. Do they make sense?\n",
    "\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ ???\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network on image features\n",
    "Earlier in this assigment we saw that training a two-layer neural network on raw pixels achieved better classification performance than linear classifiers on raw pixels. In this notebook we have seen that linear classifiers on image features outperform linear classifiers on raw pixels. \n",
    "\n",
    "For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 155)\n",
      "(49000, 154)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: Remove the bias dimension\n",
    "# Make sure to run this cell only ONCE\n",
    "print(X_train_feats.shape)\n",
    "X_train_feats = X_train_feats[:, :-1]\n",
    "X_val_feats = X_val_feats[:, :-1]\n",
    "X_test_feats = X_test_feats[:, :-1]\n",
    "\n",
    "print(X_train_feats.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for hs: 5.000000e+02, lr: 1.000000e+00 and r: 1.000000e-04, valid accuracy is: 0.554000\n",
      "for hs: 5.000000e+02, lr: 1.000000e+00 and r: 1.000000e-05, valid accuracy is: 0.548000\n",
      "for hs: 5.000000e+02, lr: 1.000000e+00 and r: 1.000000e-03, valid accuracy is: 0.573000\n",
      "for hs: 5.000000e+02, lr: 1.000000e-01 and r: 1.000000e-04, valid accuracy is: 0.588000\n",
      "for hs: 5.000000e+02, lr: 1.000000e-01 and r: 1.000000e-05, valid accuracy is: 0.594000\n",
      "for hs: 5.000000e+02, lr: 1.000000e-01 and r: 1.000000e-03, valid accuracy is: 0.594000\n",
      "for hs: 5.000000e+02, lr: 1.000000e-02 and r: 1.000000e-04, valid accuracy is: 0.598000\n",
      "for hs: 5.000000e+02, lr: 1.000000e-02 and r: 1.000000e-05, valid accuracy is: 0.597000\n",
      "for hs: 5.000000e+02, lr: 1.000000e-02 and r: 1.000000e-03, valid accuracy is: 0.593000\n",
      "for hs: 2.000000e+02, lr: 1.000000e+00 and r: 1.000000e-04, valid accuracy is: 0.542000\n",
      "for hs: 2.000000e+02, lr: 1.000000e+00 and r: 1.000000e-05, valid accuracy is: 0.564000\n",
      "for hs: 2.000000e+02, lr: 1.000000e+00 and r: 1.000000e-03, valid accuracy is: 0.569000\n",
      "for hs: 2.000000e+02, lr: 1.000000e-01 and r: 1.000000e-04, valid accuracy is: 0.609000\n",
      "for hs: 2.000000e+02, lr: 1.000000e-01 and r: 1.000000e-05, valid accuracy is: 0.600000\n",
      "for hs: 2.000000e+02, lr: 1.000000e-01 and r: 1.000000e-03, valid accuracy is: 0.605000\n",
      "for hs: 2.000000e+02, lr: 1.000000e-02 and r: 1.000000e-04, valid accuracy is: 0.604000\n",
      "for hs: 2.000000e+02, lr: 1.000000e-02 and r: 1.000000e-05, valid accuracy is: 0.604000\n",
      "for hs: 2.000000e+02, lr: 1.000000e-02 and r: 1.000000e-03, valid accuracy is: 0.609000\n",
      "for hs: 8.000000e+02, lr: 1.000000e+00 and r: 1.000000e-04, valid accuracy is: 0.558000\n",
      "for hs: 8.000000e+02, lr: 1.000000e+00 and r: 1.000000e-05, valid accuracy is: 0.553000\n",
      "for hs: 8.000000e+02, lr: 1.000000e+00 and r: 1.000000e-03, valid accuracy is: 0.574000\n",
      "for hs: 8.000000e+02, lr: 1.000000e-01 and r: 1.000000e-04, valid accuracy is: 0.599000\n",
      "for hs: 8.000000e+02, lr: 1.000000e-01 and r: 1.000000e-05, valid accuracy is: 0.596000\n",
      "for hs: 8.000000e+02, lr: 1.000000e-01 and r: 1.000000e-03, valid accuracy is: 0.596000\n",
      "for hs: 8.000000e+02, lr: 1.000000e-02 and r: 1.000000e-04, valid accuracy is: 0.594000\n",
      "for hs: 8.000000e+02, lr: 1.000000e-02 and r: 1.000000e-05, valid accuracy is: 0.597000\n",
      "for hs: 8.000000e+02, lr: 1.000000e-02 and r: 1.000000e-03, valid accuracy is: 0.595000\n",
      "Best Networks has an Accuracy of: 0.609000\n"
     ]
    }
   ],
   "source": [
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "input_dim = X_train_feats.shape[1]\n",
    "hidden_dim = 500\n",
    "num_classes = 10\n",
    "\n",
    "net = TwoLayerNet(input_dim, hidden_dim, num_classes)\n",
    "best_net = None\n",
    "\n",
    "################################################################################\n",
    "# TODO: Train a two-layer neural network on image features. You may want to    #\n",
    "# cross-validate various parameters as in previous sections. Store your best   #\n",
    "# model in the best_net variable.                                              #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "hidden_size = [500, 200, 800]\n",
    "learning_rate = [1, 1e-1, 1e-2]\n",
    "reg = [1e-4, 1e-5, 1e-3]\n",
    "\n",
    "best_acc = -1\n",
    "log = {}\n",
    "\n",
    "for hs in hidden_size:\n",
    "    for lr in learning_rate:\n",
    "        for r in reg:\n",
    "            # Train the network\n",
    "            stats = net.train(X_train_feats, y_train, X_val_feats, y_val,\n",
    "                        num_iters=1000, learning_rate=lr, reg=r)\n",
    "            \n",
    "            acc = stats['val_acc_history'][-1]\n",
    "            log[(hs, lr, r)] = acc\n",
    "            \n",
    "            # Print Log\n",
    "            print('for hs: %d, lr: %f and reg: %e, valid accuracy is: %f' % (hs, lr, r, acc))\n",
    "            \n",
    "            if acc > best_acc:\n",
    "                best_net = net\n",
    "                best_acc = acc\n",
    "                \n",
    "print('Best Networks has an Accuracy of: %f' % best_acc)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.602\n"
     ]
    }
   ],
   "source": [
    "# Run your best neural net classifier on the test set. You should be able\n",
    "# to get more than 55% accuracy.\n",
    "\n",
    "test_acc = (best_net.predict(X_test_feats) == y_test).mean()\n",
    "print(test_acc)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
